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Articles 1621 - 1650 of 1664
Full-Text Articles in Artificial Intelligence and Robotics
Approximate Strategic Reasoning Through Hierarchical Reduction Of Large Symmetric Games, Michael P. Wellman, Daniel M. Reeves, Kevin M. Lochner, Shih-Fen Cheng, Rahul Suri
Approximate Strategic Reasoning Through Hierarchical Reduction Of Large Symmetric Games, Michael P. Wellman, Daniel M. Reeves, Kevin M. Lochner, Shih-Fen Cheng, Rahul Suri
Research Collection School Of Computing and Information Systems
To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated local-effect games. An extended application to strategic reasoning about a complex trading scenario motivates the approach, and demonstrates methods for game-theoretic reasoning over incompletely-specified games at multiple levels of granularity.
Walverine: A Walrasian Trading Agent, Shih-Fen Cheng, Evan Leung, Kevin M. Lochner, Kevin O'Malley, Daniel M. Reeves, Julian L. Schvartzman, Michael P. Wellman
Walverine: A Walrasian Trading Agent, Shih-Fen Cheng, Evan Leung, Kevin M. Lochner, Kevin O'Malley, Daniel M. Reeves, Julian L. Schvartzman, Michael P. Wellman
Research Collection School Of Computing and Information Systems
TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as …
Robust Temporal Constraint Networks, Hoong Chuin Lau, Thomas Ou, Melvyn Sim
Robust Temporal Constraint Networks, Hoong Chuin Lau, Thomas Ou, Melvyn Sim
Research Collection School Of Computing and Information Systems
In this paper, we propose the Robust Temporal Constraint Network (RTCN) model for simple temporal constraint networks where activity durations are bounded by random variables. The problem is to determine whether such temporal network can be executed with failure probability less than a given 0 ≤ E ≤ 1 for each possible instantiation of the random variables, and if so. how one might find a feasible schedule with each given instantiation. The advantage of our model is that one can vary the value of ∊ to control the level of conservativeness of the solution. We present a computationally tractable and …
A Multi-Agent Approach For Solving Optimization Problems Involving Expensive Resources, Hoong Chuin Lau, H. Wang
A Multi-Agent Approach For Solving Optimization Problems Involving Expensive Resources, Hoong Chuin Lau, H. Wang
Research Collection School Of Computing and Information Systems
In this paper, we propose a multi-agent approach for solving a class of optimization problems involving expensive resources, where monolithic local search schemes perform miserably. More specifically, we study the class of bin-packing problems. Under our proposed Fine-Grained Agent System scheme, rational agents work both collaboratively and selfishly based on local search and mimic physics-motivated systems. We apply our approach to a generalization of bin-packing - the Inventory Routing Problem with Time Windows - which is an important logistics problem, and demonstrate the efficiency and effectiveness of our approach.
Justilm: Few-Shot Justification Generation For Explainable Fact-Checking Of Real-World Claims, Fengzhu Zeng, Wei Gao
Justilm: Few-Shot Justification Generation For Explainable Fact-Checking Of Real-World Claims, Fengzhu Zeng, Wei Gao
Research Collection School Of Computing and Information Systems
Justification is an explanation that supports the verdict assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by professional checkers. In this work, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for Explainable Claim verification, and introduce JustiLM, a novel few-shot retrieval-augmented language model to learn justification generation by leveraging fact-check articles as auxiliary resource during training. Our results show that JustiLM outperforms in-context learning (ICL)-enabled LMs including Flan-T5 and Llama2, and the retrieval-augmented model Atlas …
Corrective Maintenance Optimization In An Air Force, Hoong Chuin Lau, K. Y. Neo, W. C. Wan
Corrective Maintenance Optimization In An Air Force, Hoong Chuin Lau, K. Y. Neo, W. C. Wan
Research Collection School Of Computing and Information Systems
Successful military mission planning and execution depend critically on equipment serviceability and resupply. Due to the stochastic nature of demands, the forecast of optimal spares and resources needed to guarantee the level of serviceability is a complex problem, especially in a multi-echelon setting. In this paper, we propose a decision-support concept and software tool known as Corrective Maintenance Optimizer (CMO) that helps to optimize system availability, through proper allocation of spare parts, both strategically and operationally.
A Periodic-Review Inventory Model With Application To The Continuous-Review Obsolescence Problem, Yuyue Song, Hoong Chuin Lau
A Periodic-Review Inventory Model With Application To The Continuous-Review Obsolescence Problem, Yuyue Song, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper we consider a stochastic-demand periodic-review inventory model with sudden obsolescence. We characterize the structure of the optimal policy and propose a dynamic programming algorithm for computing its parameters. We then utilize this algorithm to approximate the solution to the continuous-review sudden obsolescence problem with general obsolescence distribution. We prove convergence of our approximation scheme, and demonstrate it numerically against known closed-form solutions of special cases.
Two-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Two-Echelon Repairable Item Inventory System With Limited Repair Capacity Under Nonstationary Demands, Hoong Chuin Lau, Huawei Song
Research Collection School Of Computing and Information Systems
We study a repairable item inventory system under limited repair capacity and nonstationary Poisson demands, motivated by corrective maintenance of military equipment. Our goal is to minimize the cost of both spare and repair resource allocation. We propose an efficient analytical model that combines optimization modeling and queuing theory.
Logistics Network Design With Differentiated Delivery Lead Time: A Chemical Industry Case Study, Michelle Lee Fong Cheong, Rohit Bhatnagar, Stephen C. Graves
Logistics Network Design With Differentiated Delivery Lead Time: A Chemical Industry Case Study, Michelle Lee Fong Cheong, Rohit Bhatnagar, Stephen C. Graves
Research Collection School Of Computing and Information Systems
Most logistics network design models assume exogenous customer demand that is independent of the service time or level. This paper examines the benefits of segmenting demand according to lead-time sensitivity of customers. To capture lead-time sensitivity in the network design model, we use a facility grouping method to ensure that the different demand classes are satisfied on time. In addition, we perform a series of computational experiments to develop a set of managerial insights for the network design decision making process.
Job Scheduling With Unfixed Availability Constraints, Hoong Chuin Lau, C. Zhang
Job Scheduling With Unfixed Availability Constraints, Hoong Chuin Lau, C. Zhang
Research Collection School Of Computing and Information Systems
Standard scheduling theory assumes that all machines are continuously available throughout the planning horizon. In many manufacturing and service management situations however, machines need to be maintained periodically to prevent malfunctions. During the maintenance period, a machine is not available for processing jobs. Hence, a more realistic scheduling model should take into account machine maintenance activities. In this paper, we study the problem of job scheduling with unfixed availability constraints on a single machine. We first propose a preliminary classification for the scheduling problem with unfixed availability constraints based on maintenance constraints, job characteristics and objective function. We divide our …
A Development Framework For Rapid Metaheuristics Hybridization, Hoong Chuin Lau, M. K. Lim, W. C. Wan, S. Halim
A Development Framework For Rapid Metaheuristics Hybridization, Hoong Chuin Lau, M. K. Lim, W. C. Wan, S. Halim
Research Collection School Of Computing and Information Systems
While meta-heuristics are effective for solving large-scale combinatorial optimization problems, they result from time-consuming trial-and-error algorithm design tailored to specific problems. For this reason, a software tool for rapid prototyping of algorithms would save considerable resources. This work presents a generic software framework that reduces development time through abstract classes and software reuse, and more importantly, aids design with support of user-defined strategies and hybridization of meta-heuristics. Most interestingly, we propose a novel way of redefining hybridization with the use of the "request and response" metaphor, which form an abstract concept for hybridization. Different hybridization schemes can now be formed …
A Two-Level Framework For Coalition Formation Via Optimization And Agent Negotiation, Hoong Chuin Lau, Lei Zhang
A Two-Level Framework For Coalition Formation Via Optimization And Agent Negotiation, Hoong Chuin Lau, Lei Zhang
Research Collection School Of Computing and Information Systems
We present a two-level coalition formation approach based on a centralized optimization model on the upper level, and a distributed agent-negotiation model on the lower level. This approach allows us to balance agent self-interests against a high joint utility. Experimental results show that the two-level coalition formation mechanism will increase not only the overall utility of the coalition, but also the individual utility of most participating agents. The results also suggest it is better for the agents to be partially cooperative rather than either fully cooperative or self-interested in our setting.
Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li
Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li
Research Collection School Of Computing and Information Systems
The Flexible Organizer for Competitive Intelligence (FOCI) is a personalised web intelligence system that provides an integrated platform for gathering, organising, tracking, and disseminating competitive information on the web. FOCI builds personalised information portfolios through a novel method called User-Configurable Clustering, which allows a user to personalise his/her portfolios in terms of the content as well as the organisational structure. This paper outlines the key challenges we face in personalised information management and gives a detailed account of FOCI’s underlying personalisation mechanism. For a quantitative evaluation of the system’s performance, we propose a set of performance indices based on information …
Taking Dcop To The Real World: Efficient Complete Solutions For Distributed Event Scheduling, Rajiv Maheswaran, Milind Tambe, Emma Bowring, Jonathan Pearce, Pradeep Varakantham
Taking Dcop To The Real World: Efficient Complete Solutions For Distributed Event Scheduling, Rajiv Maheswaran, Milind Tambe, Emma Bowring, Jonathan Pearce, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Distributed Constraint Optimization (DCOP) is an elegant formalism relevant to many areas in multiagent systems, yet complete algorithms have not been pursued for real world applications due to perceived complexity. To capably capture a rich class of complex problem domains, we introduce the Distributed Multi-Event Scheduling (DiMES) framework and design congruent DCOP formulations with binary constraints which are proven to yield the optimal solution. To approach real-world efficiency requirements, we obtain immense speedups by improving communication structure and precomputing best case bounds. Heuristics for generating better communication structures and calculating bound in a distributed manner are provided and tested on …
Notes On Equilibria In Symmetric Games, Shih-Fen Cheng, Daniel M. Reeves, Yevgeniy Vorobeychik, Michael P. Wellman
Notes On Equilibria In Symmetric Games, Shih-Fen Cheng, Daniel M. Reeves, Yevgeniy Vorobeychik, Michael P. Wellman
Research Collection School Of Computing and Information Systems
In a symmetric game, every player is identical with respect to the game rules. We show that a symmetric 2strategy game must have a pure-strategy Nash equilibrium. We also discuss Nash’s original paper and its generalized notion of symmetry in games. As a special case of Nash’s theorem, any finite symmetric game has a symmetric Nash equilibrium. Furthermore, symmetric infinite games with compact, convex strategy spaces and continuous, quasiconcave utility functions have symmetric pure-strategy Nash equilibria. Finally, we discuss how to exploit symmetry for more efficient methods of finding Nash equilibria.
Tournament Versus Fitness Uniform Selection, Shane Legg, Marcus Hutter, Akshat Kumar
Tournament Versus Fitness Uniform Selection, Shane Legg, Marcus Hutter, Akshat Kumar
Research Collection School Of Computing and Information Systems
In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In …
Multi-Period Multi-Dimensional Knapsack Problem And Its Application To Available-To-Promise, Hoong Chuin Lau, M. K. Lim
Multi-Period Multi-Dimensional Knapsack Problem And Its Application To Available-To-Promise, Hoong Chuin Lau, M. K. Lim
Research Collection School Of Computing and Information Systems
This paper is motivated by a recent trend in logistics scheduling, called Available-to-Promise. We model this problem as the multi-period multi-dimensional knapsack problem. We provide some properties for a special case of a single-dimensional problem. Based on insights obtained from these properties, we propose a two-phase heuristics for solving the multi-dimensional problem. We also propose a novel time-based ant colony optimization algorithm. The quality of the solutions generated is verified through experiments, where we demonstrate that the computational time is superior compared with integer programming to achieve solutions that are within a small percentage of the upper bounds.
Logistics Outsourcing And 3pl Challenges, Michelle L. F. Cheong
Logistics Outsourcing And 3pl Challenges, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Logistics has been an important part of every economy and every business entity. The worldwide trend in globalization has led to many companies outsourcing their logistics function to Third-Party Logistics (3PL) companies, so as to focus on their core competencies. This paper attempts to broadly identify and categorize the challenges faced by 3PL companies and discover potential gaps for future research. Some of the challenges will be related with the experience and information collected from interviews with two 3PL companies.
Predicting Nonlinear Network Traffic Using Fuzzy Neural Network, Zhaoxia Wang, Tingzhu Hao, Zengqiang Chen, Zhuzhi Yuan
Predicting Nonlinear Network Traffic Using Fuzzy Neural Network, Zhaoxia Wang, Tingzhu Hao, Zengqiang Chen, Zhuzhi Yuan
Research Collection School Of Computing and Information Systems
Network traffic is a complex and nonlinear process, which is significantly affected by immeasurable parameters and variables. This paper addresses the use of the five-layer fuzzy neural network (FNN) for predicting the nonlinear network traffic. The structure of this system is introduced in detail. Through training the FNN using back-propagation algorithm with inertia] terms the traffic series can be well predicted by this FNN system. We analyze the performance of the FNN in terms of prediction ability as compared with solely neural network. The simulation demonstrates that the proposed FNN is superior to the solely neural network systems. In addition, …
Task Allocation Via Multi-Agent Coalition Formation: Taxonomy, Algorithms And Complexity, Hoong Chuin Lau, L. Zhang
Task Allocation Via Multi-Agent Coalition Formation: Taxonomy, Algorithms And Complexity, Hoong Chuin Lau, L. Zhang
Research Collection School Of Computing and Information Systems
Coalition formation has become a key topic in multiagent research. In this paper, we propose a preliminary classification for the coalition formation problem based on three driving factors (demands, resources and profit objectives). We divide our analysis into 5 cases. For each case, we present algorithms and complexity results. We anticipate that with future research, this classification can be extended in similar fashion to the comprehensive classification for the job scheduling problem.
Multi-Agent Coalition Via Autonomous Price Negotiation In A Real-Time Web Environment, Hoong Chuin Lau, Wei Sian Lim
Multi-Agent Coalition Via Autonomous Price Negotiation In A Real-Time Web Environment, Hoong Chuin Lau, Wei Sian Lim
Research Collection School Of Computing and Information Systems
In e-marketplaces, customers specify job requests in real-time and agents form coalitions to service them. This paper proposes a protocol for self-interested agents to negotiate prices in forming successful coalitions. We propose and experiment with two negotiation schemes: one allows information sharing while the other does not.
Safe Robot Driving In Cluttered Environments, Chuck Thorpe, Justin Carlson, Dave Duggins, Jay Gowdy, Rob Maclachlan, Christoph Mertz, Arne Suppe, Bob Wang
Safe Robot Driving In Cluttered Environments, Chuck Thorpe, Justin Carlson, Dave Duggins, Jay Gowdy, Rob Maclachlan, Christoph Mertz, Arne Suppe, Bob Wang
Research Collection School Of Computing and Information Systems
The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The earlier work of the group concentrated on road following, cross-country driving, and obstacle detection. The new focus is on short-range sensing, to look all around the vehicle for safe driving. The current system uses video sensing, laser rangefinders, a novel light-stripe rangefinder, software to process each sensor individually, and a map-based fusion system. The complete system has been demonstrated on the Navlab 11 vehicle for monitoring the environment of a vehicle driving through a cluttered urban environment, …
Solving Multi-Objective Multi-Constrained Optimization Problems Using Hybrid Ants System And Tabu Search, Hoong Chuin Lau, Min Kwang Lim, Wee Chong Wan, Hui Wang, Xiaotao Wu
Solving Multi-Objective Multi-Constrained Optimization Problems Using Hybrid Ants System And Tabu Search, Hoong Chuin Lau, Min Kwang Lim, Wee Chong Wan, Hui Wang, Xiaotao Wu
Research Collection School Of Computing and Information Systems
Many real-world optimization problems today are multi-objective multi-constraint generalizations of NP-hard problems. A classic case we study in this paper is the Inventory Routing Problem with Time Windows (IRPTW). IRPTW considers inventory costs across multiple instances of Vehicle Routing Problem with Time Windows (VRPTW). The latter is in turn extended with time-windows constraints from the Vehicle Routing Problem (VRP), which is extended with optimal fleet size objective from the single-objective Traveling Salesman Problem (TSP). While single-objective problems like TSP are solved effectively using meta-heuristics, it is not obvious how to cope with the increasing complexity systematically as the problem is …
A Generic Object-Oriented Tabu Search Framework, Hoong Chuin Lau, Wee Chong Wan, Xiaomin Jia
A Generic Object-Oriented Tabu Search Framework, Hoong Chuin Lau, Wee Chong Wan, Xiaomin Jia
Research Collection School Of Computing and Information Systems
Presently, most tabu search designers devise their applications without considering the potential of design and code reuse, which consequently prolong the development of subsequent applications. In this paper, we propose a software solution known as Tabu Search Framework (TSF), which is a generic C++ software framework for tabu search implementation. The framework excels in code recycling through the use of a welldesigned set of generic abstract classes that clearly define their collaborative roles in the algorithm. Additionally, the framework incorporates a centralized process and control mechanism that enhances the search with intelligence. This results in a generic framework that is …
A Generic Object-Oriented Tabu Search Framework, Hoong Chuin Lau
A Generic Object-Oriented Tabu Search Framework, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Presently, most tabu search designers devise their applications without considering the potential of design and code reuse, which consequently prolong the development of subsequent applications. In this paper, we propose a software solution known as Tabu Search Framework (TSF), which is a generic C++ software framework for tabu search implementation. The framework excels in code recycling through the use of a welldesigned set of generic abstract classes that clearly define their collaborative roles in the algorithm. Additionally, the framework incorporates a centralized process and control mechanism that enhances the search with intelligence. This results in a generic framework that is …
On Machine Learning Methods For Chinese Document Classification, Ji He, Ah-Hwee Tan, Chew-Lim Tan
On Machine Learning Methods For Chinese Document Classification, Ji He, Ah-Hwee Tan, Chew-Lim Tan
Research Collection School Of Computing and Information Systems
This paper reports our comparative evaluation of three machine learning methods, namely k Nearest Neighbor (kNN), Support Vector Machines (SVM), and Adaptive Resonance Associative Map (ARAM) for Chinese document categorization. Based on two Chinese corpora, a series of controlled experiments evaluated their learning capabilities and efficiency in mining text classification knowledge. Benchmark experiments showed that their predictive performance were roughly comparable, especially on clean and well organized data sets. While kNN and ARAM yield better performances than SVM on small and clean data sets, SVM and ARAM significantly outperformed kNN on noisy data. Comparing efficiency, kNN was notably more costly …
Pickup And Delivery Problem With Time Windows: Algorithms And Test Case Generation, Hoong Chuin Lau, Zhe Liang
Pickup And Delivery Problem With Time Windows: Algorithms And Test Case Generation, Hoong Chuin Lau, Zhe Liang
Research Collection School Of Computing and Information Systems
In the pickup and delivery problem with time windows (PDPTW), vehicles have to transport loads from origins to destinations respecting capacity and time constraints. In this paper, we present a two-phase method to solve the PDPTW. In the first phase, we apply a novel construction heuristics to generate an initial solution. In the second phase, a tabu search method is proposed to improve the solution. Another contribution of this paper is a strategy to generate good problem instances and benchmarking solutions for PDPTW, based on Solomon' s benchmark test cases for VRPTW. Experimental results show that our approach yields very …
Organizing And Personalizing Intelligence Gathering From The Web, Hwee-Leng Ong, Ah-Hwee Tan, Jamie Ng, Pan Hong, Qiu-Xiang Li
Organizing And Personalizing Intelligence Gathering From The Web, Hwee-Leng Ong, Ah-Hwee Tan, Jamie Ng, Pan Hong, Qiu-Xiang Li
Research Collection School Of Computing and Information Systems
In this paper, we describe how an integrated web‐based application, code‐named FOCI (Flexible Organizer for Competitive Intelligence), can help the knowledge worker in the gathering, organizing, tracking and dissemination of competitive intelligence (CI). It combines the use of a novel user‐configurable clustering, trend analysis and visualization techniques to manage information gathered from the web. FOCI allows its users to define and personalize the organization of the information clusters according to their needs and preferences into portfolios. These personalized portfolios created are saved and can be subsequently tracked and shared with other users. The paper runs through an example to show …
Integrating Local Search And Network Flow To Solve The Inventory Routing Problem, Hoong Chuin Lau, Q Liu, H. Ono
Integrating Local Search And Network Flow To Solve The Inventory Routing Problem, Hoong Chuin Lau, Q Liu, H. Ono
Research Collection School Of Computing and Information Systems
The inventory routing problem is one of important and practical problems in logistics. It involves the integration of inventory management and vehicle routing, both of which are known to be NP-hard. In this paper, we combine local search and network flows to solve the inventory management problem, by utilizing the minimum cost flow sub-solutions as a guiding measure for local search. We then integrate with a standard VRPTW solver to present experimental results for the overall inventory routing problem, based on instances extended from the Solomon benchmark problems.
Driving In Traffic: Short-Range Sensing For Urban Collision Avoidance, Chuck Thorpe, Dave Duggins, Jay Gowdy, Rob Maclaughlin, Christoph Mertz, Mel Siegel, Arne Suppe, Bob Wang, Teruko Yata
Driving In Traffic: Short-Range Sensing For Urban Collision Avoidance, Chuck Thorpe, Dave Duggins, Jay Gowdy, Rob Maclaughlin, Christoph Mertz, Mel Siegel, Arne Suppe, Bob Wang, Teruko Yata
Research Collection School Of Computing and Information Systems
Intelligent vehicles are beginning to appear on the market, but so far their sensing and warning functions only work on the open road. Functions such as runoff-road warning or adaptive cruise control are designed for the uncluttered environments of open highways. We are working on the much more difficult problem of sensing and driver interfaces for driving in urban areas. We need to sense cars and pedestrians and curbs and fire plugs and bicycles and lamp posts; we need to predict the paths of our own vehicle and of other moving objects; and we need to decide when to issue …